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. 2024 Apr 5:15:1375110.
doi: 10.3389/fphar.2024.1375110. eCollection 2024.

Decoding the research landscape of drug resistance and therapeutic approaches in head and neck cancer: a bibliometric analysis from 2000 to 2023

Qi Han  1   2 , Junqi Shi  1   2   3 , Jiaojiao Liu  1   2   3 , Yang Fu  4 , Zhongxun Li  1   2 , Huina Guo  1   2 , Xiaoya Guan  1   2 , Xuting Xue  1   2 , Hongliang Liu  1   2   5   6 , Liting Zhao  1   2 , Chunming Zhang  1   2   5
Affiliations

Decoding the research landscape of drug resistance and therapeutic approaches in head and neck cancer: a bibliometric analysis from 2000 to 2023

Qi Han et al. Front Pharmacol. .

Abstract

Introduction: Head and neck cancer is one of the most common tumors worldwide. However, drug resistance in its treatment has become a major factor limiting the efficacy. This study aims to comprehensively understand the current status of research in this field.

Methods: The study analyzes papers related to therapeutic resistance in head and neck cancer published between 2000 and 2023 in the Web of Science Core Collection To achieve the research objectives, we searched the WoSCC for research and review papers on therapeutic resistance in head and neck cancer from 2000 to 2023, screened the English literature, and analyzed the research hotspots, academic collaborations, and trends in detail using tools such as Citespace, SCImago Graphica, and VOS viewer.

Results: This study summarizes 787 head and neck cancer treatment resistance publications from WoSCC. The analysis showed that China and the United States are the major contributors in this field, and Grandis Jennifer R and Yang Jai-Sing are the key scholars. Keyword analysis showed that "cisplatin resistance" is a continuing focus of attention, while "Metastasis" and "Ferroptosis" may be emerging research hotspots. Literature clustering analysis pointed out that "Ferroptosis", "Immunotherapy" and "ERK signaling" were the recent hotspots that received extensive attention and citations. Finally, we discuss the current status and challenges in drug-resistant therapies for head and neck cancer.

Conclusion: This study is the first comprehensive bibliometric analysis of drug resistance in head and neck cancer. Reveals current trends and helps researchers grasp cutting-edge hotspots in the field.

Keywords: bibliometric analysis; data visualization; drug resistance; head and neck cancer; research frontiers.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Literature strategy and data analysis flowchart.
FIGURE 2
FIGURE 2
Annual publication and citation trends. (A) The upper left panel shows the pattern of annual publication growth using a non-linear fitting method. Between 2000 and 2010, the blue area corresponds to a trend of flat growth in annual publications. Between 2011 and 2023, the red area reflects a markedly rapid increase in annual publications. (B) The evolution of the annual citation trend can be divided into three phases. The blue interval shows fluctuations, the red interval characterizes a period of stabilization, and the yellow interval marks a phase of declining citations.
FIGURE 3
FIGURE 3
National, institutional and author contributions. (A) In the figure, the left Y-axis shows the number of publications, the right Y-axis shows citation frequency, and the X-axis shows countries. The blue histogram indicates that China has the most publications on the X-axis. The red dots represent the standardized citation frequency, which decreases from left to right. (B) Collaboration between countries is categorised in the figure by red, blue, and green. Dot sizes reflect the number of publications by country, while connecting lines reflect cooperation intensity. (C) The figure categorizes inter-agency collaborations in red, blue, and green. Size of dots indicates volume of publications, whereas the width of connecting lines indicates intensity of cooperation.
FIGURE 4
FIGURE 4
Author contributions and cluster analysis. (A) TOP10 publications and TOP4 burst strength author visualizations. (B) Cluster analysis of TOP10 published authors yielded five clusters representing their research areas. (C) Nine significant scholars were identified through a Venn diagram analysis of TOP10 publication volume, H-index, G-index, and M-index. (D) The vertical coordinates of the graph indicate the annual number of articles and total publications by nine influential scholars. The horizontal coordinates indicate the annual number of articles. The size of the red dots reflects each scholar’s publications.
FIGURE 5
FIGURE 5
Keyword hotspots and cluster analysis. (A) The VOSviewer displays resistance keywords to head and neck cancer. Darker shades indicate a higher frequency of keywords. (B) By clustering the keywords based on N = 30, 16 clusters were obtained. As the arrows point down, the number of keywords in the clusters decreases. (C) Sixteen clusters were unfolded and labels were added using the LSI and LLR algorithms, respectively. (D) The left side of the panel shows keyword yearly frequency and the right side shows keyword cumulative frequency. While keyword frequencies have been normalized, the vertical axis represents keywords and the horizontal axis represents years.
FIGURE 6
FIGURE 6
An analysis of the literature’s impact on the field. (A) Two papers with high performance in mediation, Agrawal et al. (2011) and Siegel et al. (2019), are shown in the figure, and they are presented visually with the top10 papers in terms of citation frequency. (B) The red dots in the graph indicate papers in the network with high burst intensity. (C) The panel shows the top10 papers in terms of outbreak intensity and duration. (D) By applying MapEquation to literature with citations that lasted more than 4 years or more, we successfully visualized five articles between 2016 and 2023.
FIGURE 7
FIGURE 7
Clustering of literature based on visual analysis. (A) Literature clustering was analyzed and clusters 0#-#8 were selected to be visualized using a peaks and valleys graph. Labels obtained from LSI and LLR algorithms for clustering are vertical. Red points indicate that both algorithms agree on the labels. (B)The “Cluster Dependencies” function was used to visualize the dependencies between the nine clusters. References from A to B are indicated by arrows. (C) We abstracted the dependencies between clusters 0 to 8 and added the corresponding theme names.
FIGURE 8
FIGURE 8
Journal analysis of influential publications. (A) A dual-map overlay shows journals. Cancer Research and Clinical Cancer Research were the leading journals cited. (B) By applying Bradford’s Law to the analysis, we identified the top 20 influential journals.
FIGURE 9
FIGURE 9
Perspectives on current and future drug therapies for head and neck cancer.
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